A family of least-squares magnitude phase algorithms

This paper presents a family of least-squares algorithms for adaptive signal processing of complex-valued signals. The algorithms employ a composite cost function that allows magnitude and phase errors to be weighted differently in the parameter estimation depending on their importance, providing an opportunity for enhanced estimation performance over standard least-squares methods. We also describe a procedure for automatically adjusting this weighting based on the estimation errors themselves. Simulations show the excellent behavior of the algorithms in time-varying signal conditions.